Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study.

INTRODUCTION: In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome. METHODS: This comparative study was performed in 515 patients in the Maria Sklodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications. RESULTS: We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome. CONCLUSIONS: Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease.

Role of Vitamin C in Prophylaxis and Treatment of Gout-A Literature Review.

Gout, known as "the disease of the kings", is the most frequent type of arthritis. It results from sustained hyperuricemia that leads to monosodium urate crystal deposition in joint structures and soft tissue. Environmental factors such as diet affect the incidence of gout; there is a known relationship between the occurrence of an acute attack of gout and the consumption of alcohol and meat; and a low purine diet is a widely recognized nonpharmacological method of supplementing the treatment and preventing recurrence of arthritis. This review aims to summarize the current knowledge about the role of vitamin C in prevention and treatment of gout. A PubMed/Medline database search on the role of vitamin C in purine metabolism was done. Reports from in vitro and animal studies seem to be promising and to allow explanation of the physiological relationship between vitamin C and uric acid. Most epidemiological studies indicate a significant correlation between high vitamin C intake and lower serum uric acid levels. Despite promising observations, there are few observational and interventional studies, and their results do not clearly define the benefits of a high daily intake of vitamin C in preventing the development and recurrence of gout.